- Fellowship in 2024 at Stanford University
- Scholarship in 2019 at Cornell University
About Alex Siu’s work
Siu is a mathematician who also works in the fields of statistics and neuroscience.
His particular area of research is topological inference for neuroscience. Topological data analysis (TDA) is the analysis of topological features in data, which are non-linear, coordinate-free structures that capture pairwise and multiway interactions among different parts of a data set. TDA has helped identify a new type of cancer, and it has been applied in diverse domains like neuroscience and material science. However, topological inference remains very challenging.
Siu aims to develop statistical tests to enable rigorous inference for topological features in neuroimaging data. In particular, he will focus on the statistical inference for Mapper graphs of neural data. In mathematics, he will develop the statistical foundation by establishing consistency results for Mapper graphs of Gaussian fields. The Gaussian field is a common null model for brain activation. Extending current consistency results for Mapper graphs to the case of Gaussian fields will provide a null model for neuroimaging applications. In neuroscience, he will investigate clinical implications of the topology of brain dynamics. The Mapper graph of the dynamics of a brain exhibits a core of hub states and a periphery of task-specific states. Siu will investigate the implication of this structure on disorders like ADHD.
Biography
Siu completed both a BSc and an MPhil in mathematics at the Chinese University of Hong Kong before heading to Cornell University to study for his PhD. In 2018, Siu won a Sir Edward Youde Memorial Fellowship at the Chinese University of Hong Kong. In 2019, he was awarded a Croucher Scholarship. From 2024, a Croucher Fellowship will support his postdoctoral research work at Stanford University.